{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
   "metadata": {},
   "source": [
    "# Welcome to your first assignment!\n",
    "\n",
    "Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n",
    "            <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n",
    "            <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
    "            Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458",
   "metadata": {},
   "source": [
    "# HOMEWORK EXERCISE ASSIGNMENT\n",
    "\n",
    "Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n",
    "\n",
    "You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n",
    "\n",
    "**Benefits:**\n",
    "1. No API charges - open-source\n",
    "2. Data doesn't leave your box\n",
    "\n",
    "**Disadvantages:**\n",
    "1. Significantly less power than Frontier Model\n",
    "\n",
    "## Recap on installation of Ollama\n",
    "\n",
    "Simply visit [ollama.com](https://ollama.com) and install!\n",
    "\n",
    "Once complete, the ollama server should already be running locally.  \n",
    "If you visit:  \n",
    "[http://localhost:11434/](http://localhost:11434/)\n",
    "\n",
    "You should see the message `Ollama is running`.  \n",
    "\n",
    "If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve`  \n",
    "And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2`  \n",
    "Then try [http://localhost:11434/](http://localhost:11434/) again.\n",
    "\n",
    "If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ddd15d-a3c5-4f4e-a678-873f56162724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "\n",
    "OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
    "HEADERS = {\"Content-Type\": \"application/json\"}\n",
    "MODEL = \"llama3.2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dac0a679-599c-441f-9bf2-ddc73d35b940",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a messages list using the same format that we used for OpenAI\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bb9c624-14f0-4945-a719-8ddb64f66f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "payload = {\n",
    "        \"model\": MODEL,\n",
    "        \"messages\": messages,\n",
    "        \"stream\": False\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "479ff514-e8bd-4985-a572-2ea28bb4fa40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's just make sure the model is loaded\n",
    "\n",
    "!ollama pull llama3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42b9f644-522d-4e05-a691-56e7658c0ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If this doesn't work for any reason, try the 2 versions in the following cells\n",
    "# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n",
    "# And if none of that works - contact me!\n",
    "\n",
    "response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n",
    "print(response.json()['message']['content'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe",
   "metadata": {},
   "source": [
    "# Introducing the ollama package\n",
    "\n",
    "And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n",
    "\n",
    "Under the hood, it's making the same call as above to the ollama server running at localhost:11434"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7745b9c4-57dc-4867-9180-61fa5db55eb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ollama\n",
    "\n",
    "response = ollama.chat(model=MODEL, messages=messages)\n",
    "print(response['message']['content'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4704e10-f5fb-4c15-a935-f046c06fb13d",
   "metadata": {},
   "source": [
    "## Alternative approach - using OpenAI python library to connect to Ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23057e00-b6fc-4678-93a9-6b31cb704bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# There's actually an alternative approach that some people might prefer\n",
    "# You can use the OpenAI client python library to call Ollama:\n",
    "\n",
    "from openai import OpenAI\n",
    "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
    "\n",
    "response = ollama_via_openai.chat.completions.create(\n",
    "    model=MODEL,\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9e22da-b891-41f6-9ac9-bd0c0a5f4f44",
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   "source": [
    "## Are you confused about why that works?\n",
    "\n",
    "It seems strange, right? We just used OpenAI code to call Ollama?? What's going on?!\n",
    "\n",
    "Here's the scoop:\n",
    "\n",
    "The python class `OpenAI` is simply code written by OpenAI engineers that makes calls over the internet to an endpoint.  \n",
    "\n",
    "When you call `openai.chat.completions.create()`, this python code just makes a web request to the following url: \"https://api.openai.com/v1/chat/completions\"\n",
    "\n",
    "Code like this is known as a \"client library\" - it's just wrapper code that runs on your machine to make web requests. The actual power of GPT is running on OpenAI's cloud behind this API, not on your computer!\n",
    "\n",
    "OpenAI was so popular, that lots of other AI providers provided identical web endpoints, so you could use the same approach.\n",
    "\n",
    "So Ollama has an endpoint running on your local box at http://localhost:11434/v1/chat/completions  \n",
    "And in week 2 we'll discover that lots of other providers do this too, including Gemini and DeepSeek.\n",
    "\n",
    "And then the team at OpenAI had a great idea: they can extend their client library so you can specify a different 'base url', and use their library to call any compatible API.\n",
    "\n",
    "That's it!\n",
    "\n",
    "So when you say: `ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')`  \n",
    "Then this will make the same endpoint calls, but to Ollama instead of OpenAI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90",
   "metadata": {},
   "source": [
    "## Also trying the amazing reasoning model DeepSeek\n",
    "\n",
    "Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B.  \n",
    "This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n",
    "\n",
    "Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "!ollama pull deepseek-r1:1.5b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d3d554b-e00d-4c08-9300-45e073950a76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n",
    "\n",
    "response = ollama_via_openai.chat.completions.create(\n",
    "    model=\"deepseek-r1:1.5b\",\n",
    "    messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
   "metadata": {},
   "source": [
    "# NOW the exercise for you\n",
    "\n",
    "Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e9564e",
   "metadata": {},
   "source": [
    "1. Create a Website class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "from openai import OpenAI\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d44c6179-5c00-46a1-a068-6c6226307e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = {\n",
    " \"user-agent\": \"mozilla/5.0 (windows nt 10.0; win64; x64) applewebkit/537.36 (khtml, like gecko) chrome/117.0.0.0 safari/537.36\"\n",
    "}\n",
    "\n",
    "class Website:\n",
    "    def __init__(self, url):\n",
    "        self.url = url\n",
    "        response = requests.get(url, headers = headers)\n",
    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
    "        self.title = soup.title if soup.title else \"No title found for this website\"\n",
    "            \n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9edb034",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
    "and provides a short summary, ignoring text that might be navigation related. \\\n",
    "Respond in markdown.\"\n",
    "\n",
    "def getUserPrompt(website):\n",
    "    userPrompt = f\"You are looking at a website titled {website.title}\"\n",
    "    userPrompt += \"\\nThe contents of this website is as follows; \\\n",
    "please provide a short summary of this website in markdown. \\\n",
    "If it includes news or announcements, then summarize these too.\\n\\n\"\n",
    "    userPrompt += website.text\n",
    "    return userPrompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8581edb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getPromptMessageFor(website):\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": getUserPrompt(website)}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "caf20d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize(url):\n",
    "    website = Website(url)\n",
    "    ollamaAi = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
    "    response = ollamaAi.chat.completions.create(\n",
    "        model = \"deepseek-r1:1.5b\",\n",
    "        messages = getPromptMessageFor(website)\n",
    "    )\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5e0ed89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_summary(url):\n",
    "    summary = summarize(url)\n",
    "    display(Markdown(summary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5793933",
   "metadata": {},
   "outputs": [],
   "source": [
    "display_summary(\"https://edwarddonner.com\")"
   ]
  }
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